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openmcp-sdk: The Deployment Framework for OpenMCP

Deploy your agent from lab to production at lightning speed

Introduction & Installation ​

What is openmcp-sdk.js ​

The OpenMCP Client provides an all-in-one MCP debugging solution, which is great - but not quite exciting enough.

Why? Because we always want to turn our completed MCPs into distributable apps or deploy them to servers as function services or microservices. However, the OpenMCP Client implements all the logic for interacting with large models and using tools on the frontend, making it extremely challenging to transform MCPs into standalone applications or services tied to large models.

This is where openmcp-sdk.js comes in as a lightweight solution. It's a Node.js library that enables you to seamlessly deploy your developed MCPs and debugged workflows as agents through Node.js.

Install ​

bash
npm install openmcp-sdk
bash
yarn add openmcp-sdk
bash
pnpm add openmcp-sdk

WARNING

Currently, openmcp-sdk only supports ESM-style imports.

Usage ​

bash
mkdir clever-agent && cd clever-agent
npm init -y
npm install typescript tsx @types/node --save-dev
tsc --init

Note: openmcp-sdk now only supports ESM imports. Remember to set "type": "module" in your package.json

Then install openmcp-sdk:

bash
npm install openmcp-sdk

Now let's create a main.ts file - with just a few lines of code, we can deploy the configuration as a working agent:

typescript
import { OmAgent } from 'openmcp-sdk/service/sdk';

// create Agent
const agent = new OmAgent();
    
// Load configuration, which can be automatically generated after debugging with openmcp client
agent.loadMcpConfig('./mcpconfig.json');

// Read the debugged prompt
const prompt = await agent.getPrompt('hacknews', { topn: '5' });    

// Execute the task
const res = await agent.ainvoke({ messages: prompt });

console.log('⚙️ Agent Response', res);

mcpconfig.json can be generated from openmcp client directly, you don't have to write it by yourself. Here is the example:

json
{
    "version": "1.0.0",
    "namespace": "openmcp",
    "mcpServers": {
        "my-browser": {
            "command": "mcp",
            "args": [
                "run",
                "~/projects/openmcp-tutorial/crawl4ai-mcp/main.py"
            ],
            "description": "A MCP for long-term memory support",
            "prompts": [
                "hacknews"
            ]
        }
    },
    "defaultLLM": {
        "baseURL": "https://api.deepseek.com",
        "apiToken": "sk-xxxxxxxxxxxxxx",
        "model": "deepseek-chat"
    }
}

Run your agent with npx tsx main.ts and get example output:

[2025/6/20 20:47:31] 🚀 [crawl4ai-mcp] 1.9.1 connected
[2025/6/20 20:47:35] 🤖 Agent wants to use these tools get_web_markdown
[2025/6/20 20:47:35] 🔧 using tool get_web_markdown
[2025/6/20 20:47:39] ✓  use tools success
[2025/6/20 20:47:46] 🤖 Agent wants to use these tools get_web_markdown, get_web_markdown, get_web_markdown
[2025/6/20 20:47:46] 🔧 using tool get_web_markdown
[2025/6/20 20:47:48] ✓  use tools success
[2025/6/20 20:47:48] 🔧 using tool get_web_markdown
[2025/6/20 20:47:54] ✓  use tools success
[2025/6/20 20:47:54] 🔧 using tool get_web_markdown
[2025/6/20 20:47:57] ✓  use tools success

⚙️ Agent Response
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Contributors

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Changelog